Overview

Dataset statistics

Number of variables14
Number of observations4372
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory478.3 KiB
Average record size in memory112.0 B

Variable types

NUM14

Warnings

201101 is highly correlated with 201012 and 11 other fieldsHigh correlation
201012 is highly correlated with 201101 and 11 other fieldsHigh correlation
201102 is highly correlated with 201012 and 11 other fieldsHigh correlation
201103 is highly correlated with 201012 and 11 other fieldsHigh correlation
201104 is highly correlated with 201012 and 11 other fieldsHigh correlation
201105 is highly correlated with 201012 and 11 other fieldsHigh correlation
201106 is highly correlated with 201012 and 11 other fieldsHigh correlation
201107 is highly correlated with 201012 and 11 other fieldsHigh correlation
201108 is highly correlated with 201012 and 11 other fieldsHigh correlation
201109 is highly correlated with 201012 and 11 other fieldsHigh correlation
201110 is highly correlated with 201012 and 11 other fieldsHigh correlation
201111 is highly correlated with 201012 and 11 other fieldsHigh correlation
201112 is highly correlated with 201012 and 11 other fieldsHigh correlation
201012 is highly skewed (γ1 = 60.33692839) Skewed
201101 is highly skewed (γ1 = 61.60481322) Skewed
201102 is highly skewed (γ1 = 60.07710631) Skewed
201103 is highly skewed (γ1 = 63.40516334) Skewed
201104 is highly skewed (γ1 = 63.94016045) Skewed
201105 is highly skewed (γ1 = 61.23983916) Skewed
201106 is highly skewed (γ1 = 61.96502909) Skewed
201107 is highly skewed (γ1 = 62.97100934) Skewed
201108 is highly skewed (γ1 = 57.26313437) Skewed
201109 is highly skewed (γ1 = 57.86935705) Skewed
201110 is highly skewed (γ1 = 62.33770402) Skewed
201111 is highly skewed (γ1 = 59.62841957) Skewed
201112 is highly skewed (γ1 = 52.26357689) Skewed
CustomerID has unique values Unique
201012 has 3423 (78.3%) zeros Zeros
201101 has 3588 (82.1%) zeros Zeros
201102 has 3573 (81.7%) zeros Zeros
201103 has 3351 (76.6%) zeros Zeros
201104 has 3472 (79.4%) zeros Zeros
201105 has 3292 (75.3%) zeros Zeros
201106 has 3320 (75.9%) zeros Zeros
201107 has 3378 (77.3%) zeros Zeros
201108 has 3391 (77.6%) zeros Zeros
201109 has 3070 (70.2%) zeros Zeros
201110 has 2947 (67.4%) zeros Zeros
201111 has 2661 (60.9%) zeros Zeros
201112 has 3685 (84.3%) zeros Zeros

Reproduction

Analysis started2022-11-02 07:45:28.804262
Analysis finished2022-11-02 07:45:50.074827
Duration21.27 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

CustomerID
Real number (ℝ≥0)

UNIQUE

Distinct4372
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15299.67772
Minimum12346
Maximum18287
Zeros0
Zeros (%)0.0%
Memory size34.2 KiB
2022-11-02T02:45:50.171609image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum12346
5-th percentile12613.55
Q113812.75
median15300.5
Q316778.25
95-th percentile17984.45
Maximum18287
Range5941
Interquartile range (IQR)2965.5

Descriptive statistics

Standard deviation1722.390705
Coefficient of variation (CV)0.1125769272
Kurtosis-1.195793327
Mean15299.67772
Median Absolute Deviation (MAD)1483.5
Skewness0.0009180495309
Sum66890191
Variance2966629.742
MonotocityStrictly increasing
2022-11-02T02:45:50.287195image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
123461< 0.1%
 
162821< 0.1%
 
162951< 0.1%
 
162931< 0.1%
 
162921< 0.1%
 
162871< 0.1%
 
162841< 0.1%
 
162831< 0.1%
 
162811< 0.1%
 
162221< 0.1%
 
Other values (4362)436299.8%
 
ValueCountFrequency (%) 
123461< 0.1%
 
123471< 0.1%
 
123481< 0.1%
 
123491< 0.1%
 
123501< 0.1%
 
ValueCountFrequency (%) 
182871< 0.1%
 
182831< 0.1%
 
182821< 0.1%
 
182811< 0.1%
 
182801< 0.1%
 

201012
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct17
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4631747484
Minimum0
Maximum317
Zeros3423
Zeros (%)78.3%
Memory size34.2 KiB
2022-11-02T02:45:50.376107image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum317
Range317
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.94215154
Coefficient of variation (CV)10.67016619
Kurtosis3853.444815
Mean0.4631747484
Median Absolute Deviation (MAD)0
Skewness60.33692839
Sum2025
Variance24.42486185
MonotocityNot monotonic
2022-11-02T02:45:50.469597image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%) 
0342378.3%
 
159113.5%
 
21944.4%
 
3882.0%
 
4370.8%
 
5150.3%
 
690.2%
 
740.1%
 
112< 0.1%
 
82< 0.1%
 
Other values (7)70.2%
 
ValueCountFrequency (%) 
0342378.3%
 
159113.5%
 
21944.4%
 
3882.0%
 
4370.8%
 
ValueCountFrequency (%) 
3171< 0.1%
 
371< 0.1%
 
341< 0.1%
 
161< 0.1%
 
131< 0.1%
 

201101
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct12
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3376029277
Minimum0
Maximum240
Zeros3588
Zeros (%)82.1%
Memory size34.2 KiB
2022-11-02T02:45:50.553514image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum240
Range240
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.71304557
Coefficient of variation (CV)10.99826235
Kurtosis3973.552043
Mean0.3376029277
Median Absolute Deviation (MAD)0
Skewness61.60481322
Sum1476
Variance13.78670741
MonotocityNot monotonic
2022-11-02T02:45:50.635242image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%) 
0358882.1%
 
153012.1%
 
21603.7%
 
3531.2%
 
4190.4%
 
560.1%
 
650.1%
 
750.1%
 
1230.1%
 
111< 0.1%
 
Other values (2)2< 0.1%
 
ValueCountFrequency (%) 
0358882.1%
 
153012.1%
 
21603.7%
 
3531.2%
 
4190.4%
 
ValueCountFrequency (%) 
2401< 0.1%
 
1230.1%
 
111< 0.1%
 
91< 0.1%
 
750.1%
 

201102
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct11
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3186184812
Minimum0
Maximum191
Zeros3573
Zeros (%)81.7%
Memory size34.2 KiB
2022-11-02T02:45:50.711068image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum191
Range191
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.979498485
Coefficient of variation (CV)9.35130465
Kurtosis3840.276917
Mean0.3186184812
Median Absolute Deviation (MAD)0
Skewness60.07710631
Sum1393
Variance8.877411223
MonotocityNot monotonic
2022-11-02T02:45:50.793824image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%) 
0357381.7%
 
155512.7%
 
21663.8%
 
3400.9%
 
4150.3%
 
5140.3%
 
640.1%
 
82< 0.1%
 
151< 0.1%
 
1911< 0.1%
 
ValueCountFrequency (%) 
0357381.7%
 
155512.7%
 
21663.8%
 
3400.9%
 
4150.3%
 
ValueCountFrequency (%) 
1911< 0.1%
 
151< 0.1%
 
101< 0.1%
 
82< 0.1%
 
640.1%
 

201103
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct15
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.453568161
Minimum0
Maximum364
Zeros3351
Zeros (%)76.6%
Memory size34.2 KiB
2022-11-02T02:45:50.878078image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum364
Range364
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.577629074
Coefficient of variation (CV)12.29722356
Kurtosis4131.864275
Mean0.453568161
Median Absolute Deviation (MAD)0
Skewness63.40516334
Sum1983
Variance31.10994608
MonotocityNot monotonic
2022-11-02T02:45:50.960759image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%) 
0335176.6%
 
169916.0%
 
22004.6%
 
3641.5%
 
4270.6%
 
5110.3%
 
750.1%
 
640.1%
 
840.1%
 
102< 0.1%
 
Other values (5)50.1%
 
ValueCountFrequency (%) 
0335176.6%
 
169916.0%
 
22004.6%
 
3641.5%
 
4270.6%
 
ValueCountFrequency (%) 
3641< 0.1%
 
181< 0.1%
 
131< 0.1%
 
121< 0.1%
 
111< 0.1%
 

201104
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct13
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3989021043
Minimum0
Maximum360
Zeros3472
Zeros (%)79.4%
Memory size34.2 KiB
2022-11-02T02:45:51.062529image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum360
Range360
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.501363462
Coefficient of variation (CV)13.79126207
Kurtosis4179.344776
Mean0.3989021043
Median Absolute Deviation (MAD)0
Skewness63.94016045
Sum1744
Variance30.26499994
MonotocityNot monotonic
2022-11-02T02:45:51.163139image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%) 
0347279.4%
 
163014.4%
 
21713.9%
 
3501.1%
 
4250.6%
 
560.1%
 
750.1%
 
640.1%
 
840.1%
 
102< 0.1%
 
Other values (3)30.1%
 
ValueCountFrequency (%) 
0347279.4%
 
163014.4%
 
21713.9%
 
3501.1%
 
4250.6%
 
ValueCountFrequency (%) 
3601< 0.1%
 
121< 0.1%
 
102< 0.1%
 
91< 0.1%
 
840.1%
 

201105
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct16
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4945105215
Minimum0
Maximum313
Zeros3292
Zeros (%)75.3%
Memory size34.2 KiB
2022-11-02T02:45:51.258229image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum313
Range313
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.851934125
Coefficient of variation (CV)9.811589267
Kurtosis3939.913076
Mean0.4945105215
Median Absolute Deviation (MAD)0
Skewness61.23983916
Sum2162
Variance23.54126476
MonotocityNot monotonic
2022-11-02T02:45:51.341338image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%) 
0329275.3%
 
168315.6%
 
22475.6%
 
3751.7%
 
4320.7%
 
5150.3%
 
780.2%
 
670.2%
 
940.1%
 
830.1%
 
Other values (6)60.1%
 
ValueCountFrequency (%) 
0329275.3%
 
168315.6%
 
22475.6%
 
3751.7%
 
4320.7%
 
ValueCountFrequency (%) 
3131< 0.1%
 
251< 0.1%
 
201< 0.1%
 
161< 0.1%
 
151< 0.1%
 

201106
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct15
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4602012809
Minimum0
Maximum305
Zeros3320
Zeros (%)75.9%
Memory size34.2 KiB
2022-11-02T02:45:51.424906image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum305
Range305
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.708916056
Coefficient of variation (CV)10.23229672
Kurtosis4004.945288
Mean0.4602012809
Median Absolute Deviation (MAD)0
Skewness61.96502909
Sum2012
Variance22.17389042
MonotocityNot monotonic
2022-11-02T02:45:51.506106image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%) 
0332075.9%
 
171916.4%
 
21884.3%
 
3731.7%
 
4360.8%
 
5120.3%
 
680.2%
 
760.1%
 
840.1%
 
161< 0.1%
 
Other values (5)50.1%
 
ValueCountFrequency (%) 
0332075.9%
 
171916.4%
 
21884.3%
 
3731.7%
 
4360.8%
 
ValueCountFrequency (%) 
3051< 0.1%
 
201< 0.1%
 
161< 0.1%
 
121< 0.1%
 
101< 0.1%
 

201107
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct13
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4407593779
Minimum0
Maximum334
Zeros3378
Zeros (%)77.3%
Memory size34.2 KiB
2022-11-02T02:45:51.589444image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum334
Range334
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.129520269
Coefficient of variation (CV)11.63791521
Kurtosis4093.453966
Mean0.4407593779
Median Absolute Deviation (MAD)0
Skewness62.97100934
Sum1927
Variance26.31197819
MonotocityNot monotonic
2022-11-02T02:45:51.665029image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%) 
0337877.3%
 
166615.2%
 
21944.4%
 
3771.8%
 
4330.8%
 
580.2%
 
850.1%
 
650.1%
 
172< 0.1%
 
131< 0.1%
 
Other values (3)30.1%
 
ValueCountFrequency (%) 
0337877.3%
 
166615.2%
 
21944.4%
 
3771.8%
 
4330.8%
 
ValueCountFrequency (%) 
3341< 0.1%
 
172< 0.1%
 
131< 0.1%
 
121< 0.1%
 
850.1%
 

201108
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct16
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3973010064
Minimum0
Maximum193
Zeros3391
Zeros (%)77.6%
Memory size34.2 KiB
2022-11-02T02:45:51.748693image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum193
Range193
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.059947386
Coefficient of variation (CV)7.701836483
Kurtosis3593.620868
Mean0.3973010064
Median Absolute Deviation (MAD)0
Skewness57.26313437
Sum1737
Variance9.363278003
MonotocityNot monotonic
2022-11-02T02:45:51.840515image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%) 
0339177.6%
 
168215.6%
 
21954.5%
 
3491.1%
 
4230.5%
 
5120.3%
 
650.1%
 
930.1%
 
730.1%
 
102< 0.1%
 
Other values (6)70.2%
 
ValueCountFrequency (%) 
0339177.6%
 
168215.6%
 
21954.5%
 
3491.1%
 
4230.5%
 
ValueCountFrequency (%) 
1931< 0.1%
 
191< 0.1%
 
151< 0.1%
 
131< 0.1%
 
121< 0.1%
 

201109
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct18
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5322506862
Minimum0
Maximum250
Zeros3070
Zeros (%)70.2%
Memory size34.2 KiB
2022-11-02T02:45:51.926608image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum250
Range250
Interquartile range (IQR)1

Descriptive statistics

Standard deviation3.952082373
Coefficient of variation (CV)7.425227388
Kurtosis3636.515431
Mean0.5322506862
Median Absolute Deviation (MAD)0
Skewness57.86935705
Sum2327
Variance15.61895508
MonotocityNot monotonic
2022-11-02T02:45:52.010397image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%) 
0307070.2%
 
190120.6%
 
22455.6%
 
3902.1%
 
4320.7%
 
590.2%
 
680.2%
 
740.1%
 
102< 0.1%
 
82< 0.1%
 
Other values (8)90.2%
 
ValueCountFrequency (%) 
0307070.2%
 
190120.6%
 
22455.6%
 
3902.1%
 
4320.7%
 
ValueCountFrequency (%) 
2501< 0.1%
 
391< 0.1%
 
161< 0.1%
 
152< 0.1%
 
141< 0.1%
 

201110
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct16
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6031564501
Minimum0
Maximum375
Zeros2947
Zeros (%)67.4%
Memory size34.2 KiB
2022-11-02T02:45:52.104744image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum375
Range375
Interquartile range (IQR)1

Descriptive statistics

Standard deviation5.777790144
Coefficient of variation (CV)9.579256165
Kurtosis4036.421673
Mean0.6031564501
Median Absolute Deviation (MAD)0
Skewness62.33770402
Sum2637
Variance33.38285894
MonotocityNot monotonic
2022-11-02T02:45:52.187919image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%) 
0294767.4%
 
197922.4%
 
22816.4%
 
3872.0%
 
4400.9%
 
5170.4%
 
1150.1%
 
640.1%
 
840.1%
 
92< 0.1%
 
Other values (6)60.1%
 
ValueCountFrequency (%) 
0294767.4%
 
197922.4%
 
22816.4%
 
3872.0%
 
4400.9%
 
ValueCountFrequency (%) 
3751< 0.1%
 
291< 0.1%
 
211< 0.1%
 
171< 0.1%
 
121< 0.1%
 

201111
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct18
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7918572736
Minimum0
Maximum377
Zeros2661
Zeros (%)60.9%
Memory size34.2 KiB
2022-11-02T02:45:52.268987image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum377
Range377
Interquartile range (IQR)1

Descriptive statistics

Standard deviation5.897752096
Coefficient of variation (CV)7.447998891
Kurtosis3791.451736
Mean0.7918572736
Median Absolute Deviation (MAD)0
Skewness59.62841957
Sum3462
Variance34.78347978
MonotocityNot monotonic
2022-11-02T02:45:52.358187image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%) 
0266160.9%
 
1103723.7%
 
23828.7%
 
31563.6%
 
4621.4%
 
5290.7%
 
6180.4%
 
780.2%
 
860.1%
 
1330.1%
 
Other values (8)100.2%
 
ValueCountFrequency (%) 
0266160.9%
 
1103723.7%
 
23828.7%
 
31563.6%
 
4621.4%
 
ValueCountFrequency (%) 
3771< 0.1%
 
481< 0.1%
 
401< 0.1%
 
221< 0.1%
 
1330.1%
 

201112
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2321591949
Minimum0
Maximum94
Zeros3685
Zeros (%)84.3%
Memory size34.2 KiB
2022-11-02T02:45:52.441591image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum94
Range94
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.536809724
Coefficient of variation (CV)6.619637548
Kurtosis3173.047648
Mean0.2321591949
Median Absolute Deviation (MAD)0
Skewness52.26357689
Sum1015
Variance2.361784127
MonotocityNot monotonic
2022-11-02T02:45:52.523031image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0368584.3%
 
153612.3%
 
2982.2%
 
3390.9%
 
450.1%
 
630.1%
 
530.1%
 
91< 0.1%
 
101< 0.1%
 
941< 0.1%
 
ValueCountFrequency (%) 
0368584.3%
 
153612.3%
 
2982.2%
 
3390.9%
 
450.1%
 
ValueCountFrequency (%) 
941< 0.1%
 
101< 0.1%
 
91< 0.1%
 
630.1%
 
530.1%
 

Interactions

2022-11-02T02:45:29.415952image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:29.520614image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
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2022-11-02T02:45:29.774379image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:29.885017image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:29.995749image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:30.104830image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:30.218661image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:30.331267image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:30.433105image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:30.565018image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:30.666801image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:30.793083image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:30.916931image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:31.010905image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
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2022-11-02T02:45:34.783638image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
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2022-11-02T02:45:34.976172image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
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2022-11-02T02:45:36.570436image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:36.673668image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
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2022-11-02T02:45:36.959551image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:37.052344image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
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2022-11-02T02:45:48.722497image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:48.825354image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:48.928514image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:49.021500image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:49.131858image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:49.236435image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:49.331164image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:49.425095image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Correlations

2022-11-02T02:45:52.606695image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-02T02:45:52.756837image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-02T02:45:52.922385image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-02T02:45:53.077705image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-02T02:45:49.603363image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:49.813358image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Sample

First rows

CustomerID201012201101201102201103201104201105201106201107201108201109201110201111201112
0123460200000000000
1123471100101010101
2123481100100001000
3123490000000000010
4123500010000000000
5123520017000002010
6123530000010000000
7123540000100000000
8123550000010000000
9123560100100000010

Last rows

CustomerID201012201101201102201103201104201105201106201107201108201109201110201111201112
4362182730001000001001
4363182740000000000020
4364182760000000000120
4365182770100000000100
4366182780000000001000
4367182800001000000000
4368182810000001000000
4369182820000000020001
4370182830210112201141
4371182870000010000200